Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	58.704322
daal4py_KNeighborsClassifier	0.0	5.0	35.517624
KNeighborsClassifier_kd_tree	0.0	6.0	9.052083
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	48.677891
KMeans	0.0	11.0	30.953066
daal4py_KMeans	0.0	7.0	23.554425
LogisticRegression	0.0	1.0	9.642698
daal4py_LogisticRegression	0.0	1.0	2.843947
Ridge	0.0	0.0	1.101484
daal4py_Ridge	0.0	0.0	0.769390
total	0.0	48.0	40.890023

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.142	0.005	1000000	1000000	100	brute	-1	1	NaN	NaN	0.538	0.014	0.265	0.002	See
1	KNeighborsClassifier	predict	0.195	0.013	1000000	1	100	brute	-1	1	1.0	1.0	0.101	0.002	1.928	0.005	See
2	KNeighborsClassifier	predict	32.225	0.000	1000000	1000	100	brute	-1	1	1.0	1.0	4.470	0.041	7.209	0.000	See
3	KNeighborsClassifier	fit	0.141	0.004	1000000	1000000	100	brute	-1	5	NaN	NaN	0.535	0.009	0.263	0.001	See
4	KNeighborsClassifier	predict	0.187	0.015	1000000	1	100	brute	-1	5	1.0	1.0	0.099	0.002	1.890	0.007	See
5	KNeighborsClassifier	predict	38.648	0.000	1000000	1000	100	brute	-1	5	1.0	1.0	4.469	0.017	8.649	0.000	See
6	KNeighborsClassifier	fit	0.131	0.004	1000000	1000000	100	brute	-1	100	NaN	NaN	0.530	0.020	0.247	0.002	See
7	KNeighborsClassifier	predict	0.199	0.015	1000000	1	100	brute	-1	100	1.0	1.0	0.104	0.002	1.922	0.006	See
8	KNeighborsClassifier	predict	38.809	0.000	1000000	1000	100	brute	-1	100	1.0	1.0	4.562	0.038	8.508	0.000	See
9	KNeighborsClassifier	fit	0.129	0.006	1000000	1000000	100	brute	1	1	NaN	NaN	0.530	0.013	0.242	0.003	See
10	KNeighborsClassifier	predict	0.199	0.006	1000000	1	100	brute	1	1	1.0	1.0	0.103	0.002	1.942	0.001	See
11	KNeighborsClassifier	predict	17.218	0.001	1000000	1000	100	brute	1	1	1.0	1.0	4.470	0.032	3.852	0.000	See
12	KNeighborsClassifier	fit	0.129	0.006	1000000	1000000	100	brute	1	5	NaN	NaN	0.529	0.009	0.243	0.003	See
13	KNeighborsClassifier	predict	0.211	0.006	1000000	1	100	brute	1	5	1.0	1.0	0.099	0.003	2.120	0.002	See
14	KNeighborsClassifier	predict	25.509	0.001	1000000	1000	100	brute	1	5	1.0	1.0	4.483	0.019	5.690	0.000	See
15	KNeighborsClassifier	fit	0.141	0.005	1000000	1000000	100	brute	1	100	NaN	NaN	0.543	0.017	0.260	0.002	See
16	KNeighborsClassifier	predict	0.211	0.007	1000000	1	100	brute	1	100	1.0	1.0	0.100	0.001	2.115	0.001	See
17	KNeighborsClassifier	predict	25.386	0.079	1000000	1000	100	brute	1	100	1.0	1.0	4.541	0.027	5.591	0.000	See
18	KNeighborsClassifier	fit	0.055	0.002	1000000	1000000	2	brute	-1	1	NaN	NaN	0.098	0.005	0.562	0.004	See
19	KNeighborsClassifier	predict	0.020	0.003	1000000	1	2	brute	-1	1	1.0	1.0	0.005	0.000	4.470	0.019	See
20	KNeighborsClassifier	predict	24.208	0.237	1000000	1000	2	brute	-1	1	1.0	1.0	0.997	0.013	24.291	0.000	See
21	KNeighborsClassifier	fit	0.052	0.002	1000000	1000000	2	brute	-1	5	NaN	NaN	0.097	0.007	0.534	0.007	See
22	KNeighborsClassifier	predict	0.031	0.002	1000000	1	2	brute	-1	5	1.0	1.0	0.005	0.000	6.571	0.015	See
23	KNeighborsClassifier	predict	32.645	0.000	1000000	1000	2	brute	-1	5	1.0	1.0	1.025	0.035	31.849	0.001	See
24	KNeighborsClassifier	fit	0.056	0.002	1000000	1000000	2	brute	-1	100	NaN	NaN	0.098	0.004	0.568	0.003	See
25	KNeighborsClassifier	predict	0.031	0.003	1000000	1	2	brute	-1	100	1.0	1.0	0.005	0.001	6.450	0.020	See
26	KNeighborsClassifier	predict	32.576	0.000	1000000	1000	2	brute	-1	100	1.0	1.0	1.082	0.013	30.114	0.000	See
27	KNeighborsClassifier	fit	0.053	0.002	1000000	1000000	2	brute	1	1	NaN	NaN	0.098	0.004	0.540	0.002	See
28	KNeighborsClassifier	predict	0.013	0.001	1000000	1	2	brute	1	1	1.0	1.0	0.005	0.000	2.822	0.017	See
29	KNeighborsClassifier	predict	11.441	0.056	1000000	1000	2	brute	1	1	1.0	1.0	1.004	0.020	11.393	0.000	See
30	KNeighborsClassifier	fit	0.056	0.003	1000000	1000000	2	brute	1	5	NaN	NaN	0.097	0.004	0.575	0.005	See
31	KNeighborsClassifier	predict	0.023	0.002	1000000	1	2	brute	1	5	1.0	1.0	0.005	0.000	4.708	0.018	See
32	KNeighborsClassifier	predict	20.199	0.034	1000000	1000	2	brute	1	5	1.0	1.0	0.995	0.006	20.308	0.000	See
33	KNeighborsClassifier	fit	0.058	0.004	1000000	1000000	2	brute	1	100	NaN	NaN	0.095	0.004	0.610	0.006	See
34	KNeighborsClassifier	predict	0.022	0.001	1000000	1	2	brute	1	100	1.0	1.0	0.005	0.000	4.523	0.009	See
35	KNeighborsClassifier	predict	20.185	0.091	1000000	1000	2	brute	1	100	1.0	1.0	1.069	0.010	18.882	0.000	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	2.622	0.047	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.739	0.011	3.546	0.001	See
1	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.000	0.000	10.622	0.334	See
2	KNeighborsClassifier_kd_tree	predict	0.463	0.013	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.116	0.006	4.006	0.003	See
3	KNeighborsClassifier_kd_tree	fit	2.635	0.038	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	0.784	0.011	3.361	0.000	See
4	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.001	0.000	6.534	0.234	See
5	KNeighborsClassifier_kd_tree	predict	0.839	0.016	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.209	0.006	4.017	0.001	See
6	KNeighborsClassifier_kd_tree	fit	2.642	0.057	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.748	0.016	3.532	0.001	See
7	KNeighborsClassifier_kd_tree	predict	0.005	0.001	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.001	0.000	4.281	0.169	See
8	KNeighborsClassifier_kd_tree	predict	2.796	0.039	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.657	0.018	4.255	0.001	See
9	KNeighborsClassifier_kd_tree	fit	2.578	0.048	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.781	0.012	3.300	0.001	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	1.0	1.0	0.000	0.000	4.063	0.291	See
11	KNeighborsClassifier_kd_tree	predict	0.740	0.018	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.113	0.002	6.554	0.001	See
12	KNeighborsClassifier_kd_tree	fit	2.624	0.055	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.756	0.010	3.470	0.001	See
13	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	1	5	1.0	1.0	0.001	0.000	3.155	0.287	See
14	KNeighborsClassifier_kd_tree	predict	1.377	0.021	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.209	0.005	6.592	0.001	See
15	KNeighborsClassifier_kd_tree	fit	2.639	0.052	1000000	1000000	10	kd_tree	1	100	NaN	NaN	0.745	0.013	3.544	0.001	See
16	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	1	100	1.0	1.0	0.001	0.000	2.546	0.219	See
17	KNeighborsClassifier_kd_tree	predict	4.613	0.034	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.656	0.010	7.035	0.000	See
18	KNeighborsClassifier_kd_tree	fit	1.207	0.024	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.531	0.010	2.272	0.001	See
19	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	2	kd_tree	-1	1	1.0	0.0	0.000	0.000	14.432	0.384	See
20	KNeighborsClassifier_kd_tree	predict	0.033	0.003	1000000	1000	2	kd_tree	-1	1	1.0	0.0	0.001	0.000	34.975	0.167	See
21	KNeighborsClassifier_kd_tree	fit	1.261	0.018	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.542	0.012	2.329	0.001	See
22	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	13.504	0.224	See
23	KNeighborsClassifier_kd_tree	predict	0.033	0.003	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.001	0.000	24.740	0.104	See
24	KNeighborsClassifier_kd_tree	fit	1.219	0.035	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.522	0.011	2.335	0.001	See
25	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	14.744	0.260	See
26	KNeighborsClassifier_kd_tree	predict	0.059	0.008	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.008	0.001	7.010	0.027	See
27	KNeighborsClassifier_kd_tree	fit	1.213	0.018	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.532	0.010	2.279	0.001	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	1.0	0.0	0.000	0.000	3.944	0.518	See
29	KNeighborsClassifier_kd_tree	predict	0.030	0.001	1000000	1000	2	kd_tree	1	1	1.0	0.0	0.001	0.000	31.517	0.078	See
30	KNeighborsClassifier_kd_tree	fit	1.233	0.017	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.523	0.008	2.360	0.000	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	3.499	0.452	See
32	KNeighborsClassifier_kd_tree	predict	0.034	0.001	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.001	0.000	25.466	0.065	See
33	KNeighborsClassifier_kd_tree	fit	1.237	0.043	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.530	0.012	2.334	0.002	See
34	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	3.639	0.352	See
35	KNeighborsClassifier_kd_tree	predict	0.059	0.003	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.008	0.001	7.591	0.009	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans	fit	0.023	0.019	10000	10000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.006	0.003	3.986	1.002	See
1	KMeans	predict	0.000	0.000	10000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.925	0.294	See
2	KMeans	predict	0.000	0.000	10000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.868	0.253	See
3	KMeans	fit	0.422	0.012	10000	10000	2	full	k-means++	30	300	1	0.0	30.0	NaN	24.0	NaN	0.165	0.010	2.553	0.004	See
4	KMeans	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.388	1.446	See
5	KMeans	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.299	0.096	See
6	KMeans	fit	0.013	0.002	10000	10000	2	full	random	30	3	1	0.0	16.0	NaN	30.0	NaN	0.004	0.001	3.123	0.074	See
7	KMeans	predict	0.001	0.000	10000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.680	1.069	See
8	KMeans	predict	0.000	0.000	10000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.840	0.320	See
9	KMeans	fit	0.156	0.006	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.081	0.003	1.936	0.003	See
10	KMeans	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.701	0.336	See
11	KMeans	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.271	0.056	See
12	KMeans	fit	0.150	0.005	10000	10000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.027	0.001	5.571	0.004	See
13	KMeans	predict	0.000	0.000	10000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.671	0.400	See
14	KMeans	predict	0.001	0.000	10000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.058	0.439	See
15	KMeans	fit	1.397	0.067	10000	10000	100	full	k-means++	30	300	1	0.0	20.0	NaN	18.0	NaN	0.658	0.026	2.124	0.004	See
16	KMeans	predict	0.001	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.821	0.320	See
17	KMeans	predict	0.008	0.002	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.002	0.000	3.844	0.164	See
18	KMeans	fit	0.062	0.002	10000	10000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.026	0.001	2.388	0.003	See
19	KMeans	predict	0.000	0.000	10000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.924	0.415	See
20	KMeans	predict	0.001	0.000	10000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.416	0.247	See
21	KMeans	fit	0.401	0.030	10000	10000	100	full	random	30	300	1	0.0	27.0	NaN	22.0	NaN	0.338	0.044	1.187	0.023	See
22	KMeans	predict	0.001	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.734	0.337	See
23	KMeans	predict	0.005	0.003	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.002	0.000	2.592	0.351	See
24	KMeans	fit	0.668	0.013	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.359	0.011	1.863	0.001	See
25	KMeans	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.137	0.345	See
26	KMeans	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.689	0.369	See
27	KMeans	fit	35.895	0.000	1000000	1000000	2	full	k-means++	30	300	1	0.0	30.0	NaN	30.0	NaN	16.064	0.041	2.234	0.000	See
28	KMeans	predict	0.000	0.000	1000000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.623	0.218	See
29	KMeans	predict	0.001	0.000	1000000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.198	0.077	See
30	KMeans	fit	0.563	0.008	1000000	1000000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.313	0.006	1.801	0.001	See
31	KMeans	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.919	0.512	See
32	KMeans	predict	0.001	0.001	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.465	0.935	See
33	KMeans	fit	14.711	0.007	1000000	1000000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	6.847	0.065	2.149	0.000	See
34	KMeans	predict	0.000	0.000	1000000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.637	0.248	See
35	KMeans	predict	0.001	0.000	1000000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.180	0.143	See
36	KMeans	fit	8.058	0.096	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	4.130	0.026	1.951	0.000	See
37	KMeans	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.676	0.254	See
38	KMeans	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.050	0.193	See
39	KMeans	fit	159.252	0.000	1000000	1000000	100	full	k-means++	30	300	1	0.0	30.0	NaN	30.0	NaN	87.531	0.000	1.819	0.000	See
40	KMeans	predict	0.001	0.001	1000000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.843	1.008	See
41	KMeans	predict	0.004	0.003	1000000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.002	0.000	2.584	0.384	See
42	KMeans	fit	7.222	0.059	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	3.935	0.022	1.836	0.000	See
43	KMeans	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.754	0.828	See
44	KMeans	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.631	0.249	See
45	KMeans	fit	50.579	0.000	1000000	1000000	100	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	40.677	0.000	1.243	0.000	See
46	KMeans	predict	0.001	0.000	1000000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.168	0.525	See
47	KMeans	predict	0.010	0.001	1000000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.002	0.000	5.673	0.032	See

Plots

Shared hyperparameters:

	value
algorithm	full
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	15.528	0.065	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	15.674	0.089	0.991	0.000	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.365	0.631	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.837	0.317	See
3	LogisticRegression	fit	1.302	0.025	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	1.310	0.027	0.994	0.001	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.001	0.001	0.104	1.154	See
5	LogisticRegression	predict	0.003	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.004	0.001	0.621	0.062	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	0.051	0.002	1000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.034	0.002	1.501	0.007	See
1	Ridge	predict	0.000	0.000	1000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.613	0.693	See
2	Ridge	predict	0.000	0.000	1000	100	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.615	0.318	See
3	Ridge	fit	0.012	0.001	10000	10000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.008	0.004	1.587	0.234	See
4	Ridge	predict	0.000	0.000	10000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	1.246	0.899	See
5	Ridge	predict	0.000	0.000	10000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.758	0.459	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}